The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series (“current”) and the three most recent “prior” examinations were obtained. All “prior” examinations were interpreted as negative during the original clinical image reading, while in the “current” examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operatingcharacteristic curves monotonically increased from 0.666±0.029 to0.730±0.027 as the time-lag between the “prior” (3 to 1)and “current” examinations decreases. The maximum adjusted oddsratios were 5.63, 7.43, and 11.1 for the three “prior” (3 to 1)sets of examinations, respectively. This study demonstrated a positiveassociation between the risk scores generated by a bilateral mammographicfeature difference based risk model and an increasing trend of the near-termrisk for having mammography-detected breast cancer.
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